Semi-supervised generative adversarial networks with spatial coevolution for enhanced image generation and classification

Funding for open access charge: Universidad de Málaga / CBU

A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video

Anomalous indications in monitoring equipment on board U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship\u27s crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this thesis, algorithms have been developed for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments

LTE Optimization and Resource Management in Wireless Heterogeneous Networks

Mobile communication technology is evolving with a great pace. The development of the Long Term Evolution (LTE) mobile system by 3GPP is one of the milestones in this direction. This work highlights a few areas in the LTE radio access network where the proposed innovative mechanisms can substantially improve overall LTE system performance. In order to further extend the capacity of LTE networks, an integration with the non-3GPP networks (e.g., WLAN, WiMAX etc.) is also proposed in this work. Moreover, it is discussed how bandwidth resources should be managed in such heterogeneous networks. The work has purposed a comprehensive system architecture as an overlay of the 3GPP defined SAE architecture, effective resource management mechanisms as well as a Linear Programming based analytical solution for the optimal network resource allocation problem. In addition, alternative computationally efficient heuristic based algorithms have also been designed to achieve near-optimal performance

Image Color Correction, Enhancement, and Editing

This thesis presents methods and approaches to image color correction, color enhancement, and color editing. To begin, we study the color correction problem from the standpoint of the camera's image signal processor (ISP). A camera's ISP is hardware that applies a series of in-camera image processing and color manipulation steps, many of which are nonlinear in nature, to render the initial sensor image to its final photo-finished representation saved in the 8-bit standard RGB (sRGB) color space. As white balance (WB) is one of the major procedures applied by the ISP for color correction, this thesis presents two different methods for ISP white balancing. Afterwards, we discuss another scenario of correcting and editing image colors, where we present a set of methods to correct and edit WB settings for images that have been improperly white-balanced by the ISP. Then, we explore another factor that has a significant impact on the quality of camera-rendered colors, in which we outline two different methods to correct exposure errors in camera-rendered images. Lastly, we discuss post-capture auto color editing and manipulation. In particular, we propose auto image recoloring methods to generate different realistic versions of the same camera-rendered image with new colors. Through extensive evaluations, we demonstrate that our methods provide superior solutions compared to existing alternatives targeting color correction, color enhancement, and color editing

An intelligent decision support system for acute lymphoblastic leukaemia detection

The morphological analysis of blood smear slides by haematologists or haematopathologists is one of the diagnostic procedures available to evaluate the presence of acute leukaemia. This operation is a complex and costly process, and often lacks standardized accuracy owing to a variety of factors, including insufficient expertise and operator fatigue. This research proposes an intelligent decision support system for automatic detection of acute lymphoblastic leukaemia (ALL) using microscopic blood smear images to overcome the above barrier. The work has four main key stages. (1) Firstly, a modified marker-controlled watershed algorithm integrated with the morphological operations is proposed for the segmentation of the membrane of the lymphocyte and lymphoblast cell images. The aim of this stage is to isolate a lymphocyte/lymphoblast cell membrane from touching and overlapping of red blood cells, platelets and artefacts of the microscopic peripheral blood smear sub-images. (2) Secondly, a novel clustering algorithm with stimulating discriminant measure (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of the nucleus and cytoplasm of lymphocytic cell membranes. The SDM measures are used in conjunction with Genetic Algorithm for the clustering of nucleus, cytoplasm, and background regions. (3) Thirdly, a total of eighty features consisting of shape, texture, and colour information from the nucleus and cytoplasm of the identified lymphocyte/lymphoblast images are extracted. (4) Finally, the proposed feature optimisation algorithm, namely a variant of Bare-Bones Particle Swarm Optimisation (BBPSO), is presented to identify the most significant discriminative characteristics of the nucleus and cytoplasm segmented by the SDM-based clustering algorithm. The proposed BBPSO variant algorithm incorporates Cuckoo Search, Dragonfly Algorithm, BBPSO, and local and global random walk operations of uniform combination, and Lévy flights to diversify the search and mitigate the premature convergence problem of the conventional BBPSO. In addition, it also employs subswarm concepts, self-adaptive parameters, and convergence degree monitoring mechanisms to enable fast convergence. The optimal feature subsets identified by the proposed algorithm are subsequently used for ALL detection and classification. The proposed system achieves the highest classification accuracy of 96.04% and significantly outperforms related meta-heuristic search methods and related research for ALL detection

Detection and characterisation of vessels in retinal images.

Doctor of Philosophy in Mathematics, Statistics & Computer Science. University of KwaZulu-Natal, Durban 2015.As retinopathies such as diabetic retinopathy (DR) and retinopathy of prematurity (ROP) continue to be the major causes of blindness globally, regular retinal examinations of patients can assist in the early detection of the retinopathies. The manual detection of retinal vessels is a very tedious and time consuming task as it requires about two hours to manually detect vessels in each retinal image. Automatic vessel segmentation has been helpful in achieving speed, improved diagnosis and progress monitoring of these diseases but has been challenging due to complexities such as the varying width of the retinal vessels from very large to very small, low contrast of thin vessels with respect to background and noise due to nonhomogeneous illumination in the retinal images. Although several supervised and unsupervised segmentation methods have been proposed in the literature, the segmentation of thinner vessels, connectivity loss of the vessels and time complexity remain the major challenges. In order to address these problems, this research work investigated di erent unsupervised segmentation approaches to be used in the robust detection of large and thin retinal vessels in a timely e cient manner. Firstly, this thesis conducted a study on the use of di erent global thresholding techniques combined with di erent pre-processing and post-processing techniques. Two histogram-based global thresholding techniques namely, Otsu and Isodata were able to detect large retinal vessels but fail to segment the thin vessels because these thin vessels have very low contrast and are di cult to distinguish from the background tissues using the histogram of the retinal images. Two new multi-scale approaches of computing global threshold based on inverse di erence moment and sum-entropy combined with phase congruence are investigated to improve the detection of vessels. One of the findings of this study is that the multi-scale approaches of computing global threshold combined with phase congruence based techniques improved on the detection of large vessels and some of the thin vessels. They, however, failed to maintain the width of the detected vessels. The reduction in the width of the detected large and thin vessels results in low sensitivity rates while relatively good accuracy rates were maintained. Another study on the use of fuzzy c-means and GLCM sum entropy combined on phase congruence for vessel segmentation showed that fuzzy c-means combined with phase congruence achieved a higher average accuracy rates of 0.9431 and 0.9346 but a longer running time of 27.1 seconds when compared with the multi-scale based sum entropy thresholding combined with phase congruence with the average accuracy rates of 0.9416 and 0.9318 with a running time of 10.3 seconds. The longer running time of the fuzzy c-means over the sum entropy thresholding is, however, attributed to the iterative nature of fuzzy c-means. When compared with the literature, both methods achieved considerable faster running time. This thesis investigated two novel local adaptive thresholding techniques for the segmentation of large and thin retinal vessels. The two novel local adaptive thresholding techniques applied two di erent Haralick texture features namely, local homogeneity and energy. Although these two texture features have been applied for supervised image segmentation in the literature, their novelty in this thesis lies in that they are applied using an unsupervised image segmentation approach. Each of these local adaptive thresholding techniques locally applies a multi-scale approach on each of the texture information considering the pixel of interest in relationship with its spacial neighbourhood to compute the local adaptive threshold. The localised multi-scale approach of computing the thresholds handled the challenge of the vessels' width variation. Experiments showed significant improvements in the average accuracy and average sensitivity rates of these techniques when compared with the previously discussed global thresholding methods and state of the art. The two novel local adaptive thresholding techniques achieved a higher reduction of false vessels around the border of the optic disc when compared with some of the previous techniques in the literature. These techniques also achieved a highly improved computational time of 1.9 to 3.9 seconds to segment the vessels in each retinal image when compared with the state of the art. Hence, these two novel local adaptive thresholding techniques are proposed for the segmentation of the vessels in the retinal images. This thesis further investigated the combination of di erence image and kmeans clustering technique for the segmentation of large and thin vessels in retinal images. The pre-processing phase computed a di erence image and k-means clustering technique was used for the vessel detection. While investigating this vessel segmentation method, this thesis established the need for a difference image that preserves the vessel details of the retinal image. Investigating the di erent low pass filters, median filter yielded the best di erence image required by k-means clustering for the segmentation of the retinal vessels. Experiments showed that the median filter based di erence images combined with k-means clustering technique achieved higher average accuracy and average sensitivity rates when compared with the previously discussed global thresholding methods and the state of the art. The median filter based di erence images combined with k-means clustering technique (that is, DIMDF) also achieved a higher reduction of false vessels around the border of the optic disc when compared with some previous techniques in the literature. These methods also achieved a highly improved computational time of 3.4 to 4 seconds when compared with the literature. Hence, the median filter based di erence images combined with k-means clustering technique are proposed for the segmentation of the vessels in retinal images. The characterisation of the detected vessels using tortuosity measure was also investigated in this research. Although several vessel tortuosity methods have been discussed in the literature, there is still need for an improved method that e ciently detects vessel tortuosity. The experimental study conducted in this research showed that the detection of the stationary points helps in detecting the change of direction and twists in the vessels. The combination of the vessel twist frequency obtained using the stationary points and distance metric for the computation of normalised and nonnormalised tortuosity index (TI) measure was investigated. Experimental results showed that the non-normalised TI measure had a stronger correlation with the expert's ground truth when compared with the distance metric and normalised TI measures. Hence, a non-normalised TI measure that combines the vessel twist frequency based on the stationary points and distance metric is proposed for the measurement of vessel tortuosity